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Wen Z.,Zhengzhou University | Chen K.,Jilin University | Chen K.,Key Laboratory of Symbol Computation and Knowledge Engineering | Fan Z.,Zhengzhou University
ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings | Year: 2010

Currently, the research for the extraction of information in deep web is pretty active. Although many researchers already adopted ontology in the data extraction, many problems still exist. This paper proposed an ontology evolution based method for mining in the data area. Not only will this method solve the problem when the website only consists of one record, but it also can identify he meaning of data that has no labels. With the evolution of ontology, the extraction of data records is being more accurate. Experiments indicate that this method could improve the accuracy and efficiency of data extraction. © 2010 IEEE. Source


Ma X.,Jilin University | Ma X.,Key Laboratory of Symbol Computation and Knowledge Engineering
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

A semiconductor fabrication line dynamic scheduling(SFLDS) model combining MAS(Multi-Agent System) with multi-intelligence algorithms is presented in this paper. The proposed model is based on the improved generalized partial global planning(GPGP) and utilizes the advantages of static intelligence algorithms with dynamic MAS. A scheduling process from 'macro-scheduling to micro-scheduling to repeated- scheduling' is designed for large-scale complex problems to enable to implement an effective and widely applicable prototype system for SFLDS. Under this scheme, a set of limitation and improvement of GPGP about its structure are proposed. The improved GPGP and its model are simulated by using simulation software eM-plant. A case study is provided to examine the practicability, flexibility and robustness of the proposed scheduling approach. © 2010 Springer-Verlag. Source


Zhang J.,Jilin University | Zhang J.,Key Laboratory of Symbol Computation and Knowledge Engineering | Meng W.,Jilin University | Liu Q.,Jilin University | And 3 more authors.
Optik | Year: 2016

The driving thinking of taxi drivers is always hidden in a large amount of taxis GPS data. An efficient driving stratagem derived from taxi drivers is provided for private car drivers. The five million pieces of taxis GPS data in Nanjing, China are analyzed: firstly, the data preprocessing is conducted for the reduction measuring error of GPS data with the expurgation of the static point, the drifting point, and the relatively independent point; then, the road intersections through the regional extreme points are found to restore map with the following three algorithms: the path selection algorithm based on probability, the improved Prim path selection algorithm, and the improved Prim path selection algorithm based on probability; at last, the SPFA (Shortest Path Faster Algorithm) is applied to the measurement of the road map gained from the previous three algorithms for optimal path planning with 40 pairs of starting points and termination points, and making a comparison of the road length among three methods. Through the experimental comparison, the third method namely the improved Prim path selection algorithm based on probability which proved to be more optimal than others two methods produces an efficient driving route more accurately. © 2015 Elsevier GmbH. All rights reserved. Source


Liu H.,Zhejiang Normal University | Liu H.,Key Laboratory of Symbol Computation and Knowledge Engineering | Li M.,Zhejiang Normal University | Zhao J.,Zhejiang Normal University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

With rapid development of information technology, dimensionality of data in many applications is getting higher and higher. However, many features in the high-dimensional data are redundant. Their presence may pose a great number of challenges to traditional learning algorithms. Thus, it is necessary to develop an effective technique to remove irrelevant features from data. Currently, many endeavors have been attempted in this field. In this paper, we propose a new feature selection method by using conditional mutual information estimated dynamically. Its advantage is that it can exactly represent the correlation between features along with the selection procedure. Our performance evaluations on eight benchmark datasets show that our proposed method achieves comparable performance to other well-established feature selection algorithms in most cases. © 2011 Springer-Verlag. Source


Zhang J.,Jilin University | Zhang J.,Key Laboratory of Symbol Computation and Knowledge Engineering | Jia X.,Jilin University | Zhou Z.,Jilin University
Optik | Year: 2015

To tackle the string stability problem of a vehicle platoon, an efficient collision prevention pre-compensation control algorithm called CPPC is proposed in this paper. In the algorithm, acceleration, speed, location, communication delay and spacing errors are introduced. The safe distance between vehicles is used to keep driving safety. We evaluate our algorithm experimentally using simulation method and compared it with the no string stability control algorithm. It reveals very encouraging simulation results indicate effectiveness of the proposed approach. © 2015 Elsevier GmbH. All rights reserved. Source

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